Probabilistic analysis of phytoplankton biomass at the Frisian Inlet (NL). (20th March 2015)

• Record Type: Journal Article
• Authors: Niu, Lixia; Van Gelder, P.H.A.J.M.; Guan, Yiqing; Zhang, Changkuan; Vrijling, J.K.
• Is Part Of: Estuarine, coastal and shelf science. Volume 155(2015)
• Page Start: 29
• Page End: 37
• Publication Date: 2015-03-20
• Subjects: phytoplankton biomass -- chlorophyll a -- BLOOM II model -- probabilistic analysis -- Frisian inlet; Estuarine oceanography -- Periodicals; Coasts -- Periodicals; Estuarine biology -- Periodicals; Seashore biology -- Periodicals; Coasts; Estuarine biology; Estuarine oceanography; Seashore biology; Periodicals; 551.461805
• DOI: 10.1016/j.ecss.2014.12.049
• Languages: English
• ISSNs: 0272-7714

Abstract

Abstract: The BLOOM II model is applied in this study to interrogate the variation of phytoplankton biomass (in terms of chlorophyll a ) at the Frisian Inlet (NL), located in the north of the Netherlands. The validation results of the model show that more than 90% of the comparisons between model results and observations have a good agreement, while only 3.1% are classified as poor. Chlorophyll a is significantly correlated with the ecological indicators of Si and NH4 by observational analysis. The modelled chlorophyll a is fitted well by a normal distribution function (μ = 3.95 mg m −3, σ = 2.96 mg m −3 ). Relationships between the modelled ecological indicators are investigated. Secchi depth as another important indicator is closely linked with chlorophyll a, PAR and K d . Moreover, particular attention is paid to the phytoplankton biomass in response to nutrient availability. The decrease rate of chlorophyll a is from 16.8% to 19.2% for N-reduction, 17.4%–21.3% for P-reduction and 21.7%–28.0% for both N- and P-reduction. In the presence of uncertainty, the improved prediction of chlorophyll a is derived by Bayesian Markov Chain Monte Carlo (BMCMC) in this study. The improved prediction of chlorophyll a concentrations vary from 0.147 to 15.34 mg m −3 within the 95% confidence interval. This study emphasises the use of an ecological model to predict the variation of phytoplankton biomass, and improves the prediction with the integration of uncertainty analysis. Highlights: A reliable ecological model is introduced to predict the variation of phytoplankton biomass (in terms of chlorophyll a ). A mathematical function is used to perform the validation of the model. The predicted chlorophyll a is fitted well by a normal distribution. The study aims to improve the prediction with respect to uncertainty.